P
Pradeep Kumar
Researcher at Indian Institute of Management Lucknow
Publications - 48
Citations - 814
Pradeep Kumar is an academic researcher from Indian Institute of Management Lucknow. The author has contributed to research in topics: Cluster analysis & Recommender system. The author has an hindex of 13, co-authored 48 publications receiving 565 citations. Previous affiliations of Pradeep Kumar include Institute for Development and Research in Banking Technology & Indian Institute of Management Ahmedabad.
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Responsible Artificial Intelligence (AI) for Value Formation and Market Performance in Healthcare: the Mediating Role of Patient’s Cognitive Engagement
TL;DR: In this paper, the authors conduct a mixed-method study to identify the constituents of responsible AI in the healthcare sector and investigate its role in value formation and market performance in India.
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Flexibility in service operations: review, synthesis and research agenda
Pradeep Kumar,Ajai Pratap Singh +1 more
TL;DR: The study provides a comprehensive review of the relevant articles and identifies the theoretical gaps in the research area of service operations flexibility that can be used by academia and industry for promoting flexibility.
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Recommendation generation using personalized weight of meta-paths in heterogeneous information networks
Mukul Gupta,Pradeep Kumar +1 more
TL;DR: This work proposes a heterogeneous information network-based recommendation model called HeteroPRS for personalized top-N recommendations using binary implicit feedback that utilizes the potential of meta-information related to items to improve the effectiveness of the recommendations.
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SeqPAM: A Sequence Clustering Algorithm for Web Personalization
TL;DR: This chapter introduces a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages and proposes a new clustering algorithm, SeqPAM for clustering sequential data.
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Oslcfit (organic simultaneous LSTM and CNN Fit): A novel deep learning based solution for sentiment polarity classification of reviews
TL;DR: The key contribution of this paper is the combination of features from both a CNN and a bi-directional LSTM into a single architecture with a single optimizer, which beats existing benchmarks and scales to large datasets.